Through machine learning, researchers have repeated the historic criminology experiment of telling criminals apart from law-abiding people using facial recognition.
Physiognomy, the ability to judge a person’s character from appearance alone, has been around since ancient Greece and was widely accepted by philosophers. …

COMMENTS

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Speak!

No, just the return of "the data says what we want it to say, after we sculpt it into shape". Oh dear, let's put these two guys into a trial set of 'scientists' ranking them by 'stupidity' and see what happens when the result is "Chinese are more likely to be stupid scientists". I think a few of their colleagues might have a bone to pick or two. Skulls indeed.

Re: The return of phrenology?

No; the return of those eternal researchers who don't understand Bayes's Theorem.

(On another forum I've just managed to upset a Jewish poster who is determined that Jews are a genetically distinct group by pointing out that if you used his supposed genetic markers to identify Jews, most of the positives would be false. Even citing to him an actual, peer reviewed article posted on nih.gov didn't work. People really do cling to weak statistical associations.)

Re: The return of phrenology?

supposed genetic markers to identify Jews

You could not have chosen a more difficult case to argue. "Jewishness" is traditionally conveyed solely by the mother (if your father is a Jew and your mother is not you are not a jew as per their religious canon - you become a gentile).

As a result the "main" DNA in Jews is pretty much equivalent to the rest of the population. There is little or no difference in genetic markers between _ALL_ jews and the rest of the Caucasian population. That is no longer valid for specific Jewish groups by the way - there are some with significantly higher frequency of some recessives and different genetic makeup than the overall population. The overall "Jewish" however is not distinguishable from overall Caucasian as per that NIH paper (and many others).

This was the situation until someone finally analysed mitochondrial DNA (which is exclusively from the mother - father never provides any of it). The end results were startling - the mitochondrial DNA diversity in Jews is practically NIL - they all trace down to 4 distinct women somewhere in the Middle East ~ 2000 bc (estimates based on genetic distance). If memory serves me right, that paper is in Cell by the way. A few years back, forgot exactly which issue.

In any case - trying to argue this with a numpty in a political forum is described in the Old Testament: you are throwing pearls to pigs.

arXiv = Wikipedia

arXiv is just an wiki for academic papers. Anyone can post a paper there. As a matter of fact, real journals (if their peer reviewers are doing their jobs) will reject papers if they were posted to arXiv before being peer reviewed & accepted by the real journal.

Re: The return of phrenology?

"The end results were startling - the mitochondrial DNA diversity in Jews is practically NIL - they all trace down to 4 distinct women somewhere in the Middle East ~ 2000 bc (estimates based on genetic distance). If memory serves me right,"

Your memory doesn't serve you right - research showed that about 40% of Ashkenazi Jews had that very narrow band of mitochondrial DNA. Sephardi and Mizrahi Jews had no such association.

And this is my point. Let's assume that you decided to use that mitochondrial evidence to identify Jews. For Ashkenazi alone, you would get 60% false negatives. For the Jewish population as a whole, you would get a very high percentage of false negatives. And it was false positives (or false negatives) that my post was about, since that is the importance of Bayes's Theorem.

Re: Speak!

But “Unlike a human examiner/judge, a computer vision algorithm or classifier has absolutely no subjective baggages, having no emotions, no biases whatsoever due to past experience, race, religion, political doctrine, gender, age, etc, no mental fatigue, no preconditioning of a bad sleep or meal,”.

Re: The return of phrenology?

"On another forum I've just managed to upset a Jewish poster who is determined that Jews are a genetically distinct group by pointing out that if you used his supposed genetic markers to identify Jews"

I went to school in north Norfolk with a girl (born locally) who converted to Judaism. Fairly sure her DNA remained unchanged.

Re: Casting pearls before swine.

Re: The return of phrenology?

Yep, but much better: soon there will be an app for this, and an IoT gizmo with a camera that can tell you if whoever is in your doorstep is a criminal or not. Hopefully these will be developed using DevOps so we know who the real criminals are.

Re: The return of phrenology?

>I went to school in north Norfolk with a girl (born locally) who converted to Judaism. Fairly sure her DNA remained unchanged.

There was a human-interest story on Radio 4 earlier in the year about a British woman who wanted to convert to Judaism. Her conversion was recognised by the appropriate bodies in Israel, but not by those in the U.K.

I'm not sure that says anything about Judaism other than a group of people spread across dozens of countries for hundreds of years entertain a variety of views about things, whodafunkit.

Re: Quality research

Re: Quality research

Previous studies in the area had datasets in excess of 100000 - the whole "bio-measurement" (as it is incorrect to call it biometrics) database from criminal identification using the Lambroso method has been fed into statistical analysis a gazillion times.

Each and every time the idea that "this persons shape equates to increased probability of criminality" has failed to pass more detailed statistical analysis.

Re: Quality research

dataset

Perhaps we might want to more carefully rephrase 'half of whom were convicted criminals' into 'half of whom had been convicted of a crime' - since the facial features in question might simply be attracting unwanted attention from biases within the police, whom then attempt to (and succeed) in convicting that facial type more often, regardless of the degree of "proof".

Re: dataset

Re: dataset

That's basically what I was going to say. In a country where being black makes you more likely to be convicted of a crime, a classifier like this will show that being black makes you more likely to be a criminal.

Re: dataset

Hey we know all most Uighers are deviant criminal scum, so we showed the AI pictures of Uigher criminals and the control group were all Han. So now the computer says all Uighers are criminals and all Han are non-criminals.

Re: dataset

Re: dataset

... And it's actually been tested somewhere. There was a small excercise with people who were facing court, where they gave unfortunate-looking people plastic surgery before their court case, which, as expected, gave them a lower conviction rate.

But, (and this was the interesting bit), did not reduce the rate at which previously-charged people were re-arrested. Being good-looking doesn't make criminals less criminal: it just makes them less likely to be convicted.

Re: dataset

>Being good-looking doesn't make criminals less criminal: it just makes them less likely to be convicted.

Yes and no. I take your point, but all things being equal, good-looking people have less motivation to commit crime. My reasoning is based on all the studies that suggest that good-looking people are more likely to be promoted at work, or attract more sexual partners. Therefore they can fulfil their needs without resorting to criminal behaviour. *

It's a bit like psychopaths - most aren't convicted criminals, because they can get all they want by manipulating people within the letter of law (if not the spirit), so they have no need to risk breaking any laws. As a result, most psychopaths are to be found in upper-middle management and not behind bars.

* There's a great episode of 30 Rock in which John Hamm's character is made to realise that people only think that he is competent at things (tennis, being a medical doctor, cooking, riding a motorcycle) because he is really, really good looking. He's 'in the bubble', which causes him to think that people are all just really nice and accommodating.

Re: dataset

There is actually quite a lot of scope of sample bias here. The characteristics the article describes sound quite a lot like the facial type you see with Foetal Alcohol Spectrum Disorder, i.e. children whose mothers boozed heavily during pregnancy.

People with FASD are basically damaged in a lot of ways. Facial features are altered, and brain function is compromised. These people are more likely than the general population to be criminals, and there's a fairly good chance that police consciously or subconsciously recognise this facial type as a likely sort to check for criminal activity, hence these people are going to feature disproportionately in the database.

I somehow can't blame the dataset.. neither the size nor the content. However it's more likely the programming behind the AI. Computers only do what we tell them to do and if the programmers on this particular bit let their prejudices or pre-conceived notions take sway, then the results are already pre-determined.

Nope, it is not a single dataset. It's been constructed from two separate sources, one entirely composed of criminals and another composed of people of unknown criminal stature. The criminal photos were taken from wanted pictures and the non-criminal photos taken from the internet. The two sets are completely incomparable. There's also the issue that the authors can't actually state whether the people in the non-criminal group are or are not criminals. The classifier only learned to distinguish wanted photos from internet selfies.

me thinks

Sigh...

Yeah, we've been building these classifiers for 20 years now and we keep having to teach the idiots how to use them properly (even the ones that have PhDs). Obvious problems:

Their dataset has a prior probability of criminality around 50%. That's way higher than normal and leads the system to think that criminality is common. Same problem with a lot of diagnostic medicine ANNs. They try to detect rare diseases with an equal handful of normal and diseased cases. They look great in the literature, but never get adopted, because they keep flagging up healthy people--they've been heavily biased to think that the problem exists.

Second problem is the data. Are the pictures random? I doubt it. They've started by just looking at Han Chinese. Then they picked pictures of non-criminals by browsing the web and picked pictures of criminals by scouring for wanted posters. Looking at their conclusion faces, I can easily classify criminals vs. non-criminals simply by noticing whether the person is smiling.

Third problem is feature selection. I'm sure the algorithm didn't automatically choose to look at facial features. A raw ANN will pick out tons of useless data like the color of the pixel at location (3,42). It won't automatically go looking for complex features like facial feature ratios. So, the authors picked out a bunch of features they thought might be relevant (neo-phrenology as previously noted) and discovered that some of them were more relevant than others

From this paper, I would conclude that Chinese people tend to post pictures of smiling people online and criminals tend to look unhappy in mugshots. Thus, it's easy to distinguish between a selfie and a mugshot.

Re: Sigh...

"... teach the idiots how to use them properly (even the ones that have PhDs). Obvious problems:"

We see things here, between us, of markedly differing severity.

"Their dataset has a prior probability of criminality around 50%. That's way higher than normal and leads the system to think that criminality is common." And "Same problem with a lot of diagnostic medicine ANNs. They try to detect rare diseases with an equal handful of normal and diseased cases. They look great in the literature, but never get adopted, because they keep flagging up healthy people--they've been heavily biased to think that the problem exists."

I'm not sure at all that this is relevant, especially the first bit. Training on the examples is best done with near equal numbers of samples for each class: otherwise there is likely to be criticism on that very issue. Evaluation is, likewise, best done on datasets of near equal class size; and it's easier with equal-size evaluation sets.

For operational use: Bayesian statistics does indeed require weighting with the real-life class occurrence rates - this can be dealt with totally outside of class-specific modelling. This by use of the a priori knowledge of class occurrence statistics.

"Second problem is the data. Are the pictures random? I doubt it."

Read the paper, as linked. It is much better than you (think and) write, though it does have its deficiencies.

They've started by just looking at Han Chinese.

Looking within one racial characteristic (especially on such a small dataset) it actually sound science.

"Then they picked pictures of non-criminals by browsing the web and picked pictures of criminals by scouring for wanted posters."

No! Read the paper. All the photos are from non-criminal identification sources. I suspect this is from existing photos on ID cards or driving licences, or similar. Whilst this is not ideal, there is no bias in data-capture mechanism or in the likely 'happiness' of the subjects.

"Looking at their conclusion faces, I can easily classify criminals vs. non-criminals simply by noticing whether the person is smiling."

No you cannot: see above!

However, there is a problem with the demographics, particularly of the non-criminal dataset. There is a high preponderance of university-educated people. I suspect (only suspect) that this is derived from using current students/staff and their spouses or near-spouses. Note in the paper, the collared shirts of the non-criminals and the non-collared shirts of the criminals. Some clear demographic selection would have been useful here: most likely on employment status and earnings for the non-criminals; also on the type of crime for the criminals: violence against the person, violence against property, white-collar crimes - and so on.

"So, the authors picked out a bunch of features they thought might be relevant (neo-phrenology as previously noted) and discovered that some of them were more relevant than others."

Again, so what? Whether the individual or composite features are designed manually or automatically matters nothing, providing their training and the evaluation is unbiased (including lack of bias by repeated manual feedback).

"From this paper, I would conclude that Chinese people tend to post pictures of smiling people online and criminals tend to look unhappy in mugshots. Thus, it's easy to distinguish between a selfie and a mugshot."

Vindicated!

Hold on

I am going to keep from rendering my opinion until the results are in from scanning politicians. Please be sure to tweak the algorithm properly before doing so. After all, that is how this stuff works, yes?

Big data reinvents obvious old tech

What they've trained their AI to recognize is the physical symptoms shown in children of drug and alcohol abusers; symptoms of DNA damage. Doctors can recognize these features too. They can throw all the computing power in the world at this problem but the prediction accuracy isn't going to get any better. The correlation is limited and there's nothing more in the data to process. Does the Shanghai Jiao Tong University not have medical books that could have prevented this wasted research?